Assignment 9
Durga Pokharel
04/01/2022
R Markdown
[Q.N.1] Attach the AdultUCI data in R
library(arulesViz)## Warning: package 'arulesViz' was built under R version 4.1.2## Warning: package 'arules' was built under R version 4.1.2library(arules)Here I loaded the necessary library arulesViz and arules.
data("AdultUCI") Loaded the builtit data AdultUCI.
[Q.N.2] Check the class, structure, dimension, head and tail of the attached data and write interpretations
class(AdultUCI)## [1] "data.frame"Class of “AdultUCI” is data frame.
str(AdultUCI)## 'data.frame': 48842 obs. of 15 variables:
## $ age : int 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : Factor w/ 8 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
## $ fnlwgt : int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
## $ education : Ord.factor w/ 16 levels "Preschool"<"1st-4th"<..: 14 14 9 7 14 15 5 9 15 14 ...
## $ education-num : int 13 13 9 7 13 14 5 9 14 13 ...
## $ marital-status: Factor w/ 7 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
## $ occupation : Factor w/ 14 levels "Adm-clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
## $ relationship : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
## $ race : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
## $ capital-gain : int 2174 0 0 0 0 0 0 0 14084 5178 ...
## $ capital-loss : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hours-per-week: int 40 13 40 40 40 40 16 45 50 40 ...
## $ native-country: Factor w/ 41 levels "Cambodia","Canada",..: 39 39 39 39 5 39 23 39 39 39 ...
## $ income : Ord.factor w/ 2 levels "small"<"large": 1 1 1 1 1 1 1 2 2 2 ...From above results we can see that most of variables of AdultUCI are num some of them are factor and rest of them are ord.factor type.
dim(AdultUCI)## [1] 48842 15In AdultUCI there are 48842 numbers of row and 15 variables.
head(AdultUCI)## age workclass fnlwgt education education-num marital-status
## 1 39 State-gov 77516 Bachelors 13 Never-married
## 2 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse
## 3 38 Private 215646 HS-grad 9 Divorced
## 4 53 Private 234721 11th 7 Married-civ-spouse
## 5 28 Private 338409 Bachelors 13 Married-civ-spouse
## 6 37 Private 284582 Masters 14 Married-civ-spouse
## occupation relationship race sex capital-gain capital-loss
## 1 Adm-clerical Not-in-family White Male 2174 0
## 2 Exec-managerial Husband White Male 0 0
## 3 Handlers-cleaners Not-in-family White Male 0 0
## 4 Handlers-cleaners Husband Black Male 0 0
## 5 Prof-specialty Wife Black Female 0 0
## 6 Exec-managerial Wife White Female 0 0
## hours-per-week native-country income
## 1 40 United-States small
## 2 13 United-States small
## 3 40 United-States small
## 4 40 United-States small
## 5 40 Cuba small
## 6 40 United-States smallHead(AdultUCI) shows the top 6 row with all variables.
tail(AdultUCI)## age workclass fnlwgt education education-num marital-status
## 48837 33 Private 245211 Bachelors 13 Never-married
## 48838 39 Private 215419 Bachelors 13 Divorced
## 48839 64 <NA> 321403 HS-grad 9 Widowed
## 48840 38 Private 374983 Bachelors 13 Married-civ-spouse
## 48841 44 Private 83891 Bachelors 13 Divorced
## 48842 35 Self-emp-inc 182148 Bachelors 13 Married-civ-spouse
## occupation relationship race sex capital-gain
## 48837 Prof-specialty Own-child White Male 0
## 48838 Prof-specialty Not-in-family White Female 0
## 48839 <NA> Other-relative Black Male 0
## 48840 Prof-specialty Husband White Male 0
## 48841 Adm-clerical Own-child Asian-Pac-Islander Male 5455
## 48842 Exec-managerial Husband White Male 0
## capital-loss hours-per-week native-country income
## 48837 0 40 United-States <NA>
## 48838 0 36 United-States <NA>
## 48839 0 40 United-States <NA>
## 48840 0 50 United-States <NA>
## 48841 0 40 United-States <NA>
## 48842 0 60 United-States <NA>tail(AdultUCI) gives the bottom 6 rows with all variables.
[Q.N.3]Remove “fnlwgt” and “education-num” variables from the attached data and explain the logic you have used here
AdultUCI[['fnlwgt']] <- NULL
AdultUCI[["education-num"]] <-NULLIf we want to delete the particular column we set the valeas NULL to that column. Then that column will be removed. In above two columns fnlwgt and education-num deleted.
str(AdultUCI$age)## int [1:48842] 39 50 38 53 28 37 49 52 31 42 ...[Q.N.4] Convert “age” as ordered factor variables with cuts at 15, 25, 45, 65 and 100 and label it as “Young”, “Middle-aged”, “Senior” and “Old”
AdultUCI[[ "age"]] <- ordered(cut(AdultUCI[[ "age"]], c(15,25,45,65,100)),
labels = c("Young", "Middle-aged", "Senior", "Old"))Initially our age variable is in int data type.Here we changed the age variable in four order factors like Young, Middle-age, Senior,Old.
[Q.N.5] Convert the “hours-per-week” as ordered factor variable with cuts at 0, 25, 40, 60, 168 and label it as “Part-time”, “Full-time”, “Over-time” and “Workaholic”
AdultUCI[[ "hours-per-week"]] <- ordered(cut(AdultUCI[[ "hours-per-week"]],
c(0,25,40,60,168)),
labels = c("Part-time", "Full-time", "Over-time", "Workaholic"))Initially our hours-per-week variable is in int data type.Here we changed the hours-per-week variable in four order factors like Part-time, Full-time, Over-time,Workaholic.
[Q.N.6] Convert the “capital-loss” as ordered factor variable with cuts at –Inf, 0, median and Inf and label it as “None”, “Low” and “High”
AdultUCI[[ "capital-gain"]] <- ordered(cut(AdultUCI[[ "capital-gain"]],
c(-Inf,0,median(AdultUCI[[ "capital-gain"]][AdultUCI[[ "capital-gain"]]>0]),
Inf)), labels = c("None", "Low", "High"))Initially,capital-gain variable is in int data type. Here we changed the hours-per-week variable in four order factors like None, Low, High.
[Q.N.7] Convert the “capital-gain” as ordered factor variable with cuts at –Inf, 0, median and Inf and label it as “None”, “Low” and “High”
AdultUCI[[ "capital-loss"]] <- ordered(cut(AdultUCI[[ "capital-loss"]],
c(-Inf,0, median(AdultUCI[[ "capital-loss"]][AdultUCI[[ "capital-loss"]]>0]),
Inf)), labels = c("None", "Low", "High"))Initially capital-loss variable is in int data type.Here we changed the capital-loss variable in three order factors like None, Low, High.
[Q.N.8] Create transactions of AdultUCI data as “Adult” and check it with “Adult” command
Adult <- as(AdultUCI, "transactions")
Adult## transactions in sparse format with
## 48842 transactions (rows) and
## 115 items (columns)Here we create the transition of AdultUCI data. Where transactions in sparse format with 48842 transactions (rows) and 115 items.
[Q.N.9] Inspect head and tail of the “Adult” and interpret them carefully
summary(Adult)## transactions as itemMatrix in sparse format with
## 48842 rows (elements/itemsets/transactions) and
## 115 columns (items) and a density of 0.1089939
##
## most frequent items:
## capital-loss=None capital-gain=None
## 46560 44807
## native-country=United-States race=White
## 43832 41762
## workclass=Private (Other)
## 33906 401333
##
## element (itemset/transaction) length distribution:
## sizes
## 9 10 11 12 13
## 19 971 2067 15623 30162
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 12.00 13.00 12.53 13.00 13.00
##
## includes extended item information - examples:
## labels variables levels
## 1 age=Young age Young
## 2 age=Middle-aged age Middle-aged
## 3 age=Senior age Senior
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3Three typesof transation T1, T2, T3 created in AdultUCI data.
[Q.N.10]Inspect head and tail of the “Adult” and interpret them carefully
df1 <- head(Adult)
inspect(df1)## items transactionID
## [1] {age=Middle-aged,
## workclass=State-gov,
## education=Bachelors,
## marital-status=Never-married,
## occupation=Adm-clerical,
## relationship=Not-in-family,
## race=White,
## sex=Male,
## capital-gain=Low,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States,
## income=small} 1
## [2] {age=Senior,
## workclass=Self-emp-not-inc,
## education=Bachelors,
## marital-status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Husband,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Part-time,
## native-country=United-States,
## income=small} 2
## [3] {age=Middle-aged,
## workclass=Private,
## education=HS-grad,
## marital-status=Divorced,
## occupation=Handlers-cleaners,
## relationship=Not-in-family,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States,
## income=small} 3
## [4] {age=Senior,
## workclass=Private,
## education=11th,
## marital-status=Married-civ-spouse,
## occupation=Handlers-cleaners,
## relationship=Husband,
## race=Black,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States,
## income=small} 4
## [5] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital-status=Married-civ-spouse,
## occupation=Prof-specialty,
## relationship=Wife,
## race=Black,
## sex=Female,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=Cuba,
## income=small} 5
## [6] {age=Middle-aged,
## workclass=Private,
## education=Masters,
## marital-status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Wife,
## race=White,
## sex=Female,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States,
## income=small} 6df2<-tail(Adult)
inspect(df2)## items transactionID
## [1] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital-status=Never-married,
## occupation=Prof-specialty,
## relationship=Own-child,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States} 48837
## [2] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital-status=Divorced,
## occupation=Prof-specialty,
## relationship=Not-in-family,
## race=White,
## sex=Female,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States} 48838
## [3] {age=Senior,
## education=HS-grad,
## marital-status=Widowed,
## relationship=Other-relative,
## race=Black,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States} 48839
## [4] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital-status=Married-civ-spouse,
## occupation=Prof-specialty,
## relationship=Husband,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Over-time,
## native-country=United-States} 48840
## [5] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital-status=Divorced,
## occupation=Adm-clerical,
## relationship=Own-child,
## race=Asian-Pac-Islander,
## sex=Male,
## capital-gain=Low,
## capital-loss=None,
## hours-per-week=Full-time,
## native-country=United-States} 48841
## [6] {age=Middle-aged,
## workclass=Self-emp-inc,
## education=Bachelors,
## marital-status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Husband,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## hours-per-week=Over-time,
## native-country=United-States} 48842[Q.N.11] Create absolute and relative item frequency plot and color it with RColorBrewer package
library(RColorBrewer)
coul <- brewer.pal(5, "BuPu")
itemFrequencyPlot(df1, topN=10, cex.names=1, col = coul)
Absolute frequency indicates the number of occurrences of a data value or the number of times a data value occurs.Relative frequency is the absolute frequency of that event divided by the total number of event.
{Q.N.12} Create an apriori rule as “association.rule” with support = 1%, confidence = 80% and maximum length of the rule as 10. Get summary of this rule and interpret it carefully.
rules <- apriori(df2,
parameter = list(supp=0.1, conf=0.8,
maxlen=10,
target= "rules"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.1 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 0
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[28 item(s), 6 transaction(s)] done [0.00s].
## sorting and recoding items ... [28 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, :
## Mining stopped (maxlen reached). Only patterns up to a length of 10 returned!## done [0.01s].
## writing ... [103788 rule(s)] done [0.02s].
## creating S4 object ... done [0.05s].In data AdultUCI Apriori algorithms gives 103788 rules. # [Q.N.13] Inspect the first 10 rules and interpret it critically.
inspect(head(sort(rules, by="support"),10))## lhs rhs support
## [1] {} => {capital-loss=None} 1.0000000
## [2] {} => {native-country=United-States} 1.0000000
## [3] {capital-loss=None} => {native-country=United-States} 1.0000000
## [4] {native-country=United-States} => {capital-loss=None} 1.0000000
## [5] {} => {sex=Male} 0.8333333
## [6] {} => {capital-gain=None} 0.8333333
## [7] {} => {education=Bachelors} 0.8333333
## [8] {} => {age=Middle-aged} 0.8333333
## [9] {sex=Male} => {capital-loss=None} 0.8333333
## [10] {capital-loss=None} => {sex=Male} 0.8333333
## confidence coverage lift count
## [1] 1.0000000 1.0000000 1 6
## [2] 1.0000000 1.0000000 1 6
## [3] 1.0000000 1.0000000 1 6
## [4] 1.0000000 1.0000000 1 6
## [5] 0.8333333 1.0000000 1 5
## [6] 0.8333333 1.0000000 1 5
## [7] 0.8333333 1.0000000 1 5
## [8] 0.8333333 1.0000000 1 5
## [9] 1.0000000 0.8333333 1 5
## [10] 0.8333333 1.0000000 1 5From above rules we see that capital-loss in factor order “None” are 6, native country united states are 6.
[Q.N.14] Remove the empty rules from the “association.rule” and inspect the first 10 rules with interpretations.
rules <- apriori(df2,
parameter = list(supp=0.1,
conf=0.8,
maxlen=10,
minlen=2,
target= "rules"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 0
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[28 item(s), 6 transaction(s)] done [0.00s].
## sorting and recoding items ... [28 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, :
## Mining stopped (maxlen reached). Only patterns up to a length of 10 returned!## done [0.00s].
## writing ... [103782 rule(s)] done [0.02s].
## creating S4 object ... done [0.05s].Here we removed the rules which are empty.
[Q.N.15] Create a new rule as “capital.gain.rhs.rule” with “capital-gain=None” in the RHS with support of 1%, confidence of 80%, maximum length of 10 and minimum length of 2.
capital_gain_rhs_rule<-apriori(df2,
parameter = list(supp=0.1, conf=0.8,
maxlen=10,
minlen=2),
appearance = list(default="lhs",
rhs="capital-gain=None"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 0
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[28 item(s), 6 transaction(s)] done [0.00s].
## sorting and recoding items ... [28 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, :
## Mining stopped (maxlen reached). Only patterns up to a length of 10 returned!## done [0.00s].
## writing ... [7315 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].Here I create the new rules in which we set any values in lhs but having capital-gain=None in rhs.
#[Q.N.16] Get summary of this rule and interpret it critically.
summary(capital_gain_rhs_rule)## set of 7315 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3 4 5 6 7 8 9 10
## 20 132 478 1090 1665 1758 1295 657 220
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 6.000 7.000 6.639 8.000 10.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.1667 Min. :0.8000 Min. :0.1667 Min. :0.960
## 1st Qu.:0.1667 1st Qu.:1.0000 1st Qu.:0.1667 1st Qu.:1.200
## Median :0.1667 Median :1.0000 Median :0.1667 Median :1.200
## Mean :0.1877 Mean :0.9995 Mean :0.1882 Mean :1.199
## 3rd Qu.:0.1667 3rd Qu.:1.0000 3rd Qu.:0.1667 3rd Qu.:1.200
## Max. :0.8333 Max. :1.0000 Max. :1.0000 Max. :1.200
## count
## Min. :1.000
## 1st Qu.:1.000
## Median :1.000
## Mean :1.126
## 3rd Qu.:1.000
## Max. :5.000
##
## mining info:
## data ntransactions support confidence
## df2 6 0.1 0.8
## call
## apriori(data = df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "capital-gain=None"))set of 1715 rules are formed.
[Q.N.17] Create a new rule as “hours.per.week.ft.rule” with “hour-per-week=Full-time” in the RHS with support of 1%, confidence of 80%, maximum length of 10 and minimum length of 2.
hours.per.week.ft.rule<-apriori(df2,
parameter = list(supp=0.1, conf=0.8,
maxlen=10,
minlen=2),
appearance = list(default="lhs",
rhs="hours-per-week=Full-time"))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.1 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 0
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[28 item(s), 6 transaction(s)] done [0.00s].
## sorting and recoding items ... [28 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, :
## Mining stopped (maxlen reached). Only patterns up to a length of 10 returned!## done [0.01s].
## writing ... [5676 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].Here new rules “hours.per.week.ft.rule” crested with any values in lhs and in rhs hours-per-week=Full-time data kept.
[Q.N.18] Get summary of this rule and interpret it critically.
summary(hours.per.week.ft.rule)## set of 5676 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3 4 5 6 7 8 9 10
## 13 105 390 874 1304 1350 981 494 165
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 6.000 7.000 6.605 8.000 10.000
##
## summary of quality measures:
## support confidence coverage lift count
## Min. :0.1667 Min. :1 Min. :0.1667 Min. :1.5 Min. :1.000
## 1st Qu.:0.1667 1st Qu.:1 1st Qu.:0.1667 1st Qu.:1.5 1st Qu.:1.000
## Median :0.1667 Median :1 Median :0.1667 Median :1.5 Median :1.000
## Mean :0.1695 Mean :1 Mean :0.1695 Mean :1.5 Mean :1.017
## 3rd Qu.:0.1667 3rd Qu.:1 3rd Qu.:0.1667 3rd Qu.:1.5 3rd Qu.:1.000
## Max. :0.3333 Max. :1 Max. :0.3333 Max. :1.5 Max. :2.000
##
## mining info:
## data ntransactions support confidence
## df2 6 0.1 0.8
## call
## apriori(data = df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "hours-per-week=Full-time"))5676 rules formed.
#[Q.N.19] Get new rule of “hours.per.week.ft.rule” as “conf.sort.rule” by sorting “hours.per.week.ft.rule” in descending order by “confidence” and inspect the head and tail rules with critical interpretation.
conf.sort.rule <- sort(hours.per.week.ft.rule,
by = "confidence",
decreasing = TRUE)
inspect(head(conf.sort.rule))## lhs rhs support confidence coverage lift count
## [1] {race=Black} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [2] {relationship=Other-relative} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [3] {marital-status=Widowed} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [4] {education=HS-grad} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [5] {age=Senior} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [6] {capital-gain=Low} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1inspect(tail(conf.sort.rule))## lhs rhs support confidence coverage lift count
## [1] {age=Middle-aged,
## workclass=Private,
## occupation=Prof-specialty,
## relationship=Own-child,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## native-country=United-States} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [2] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## occupation=Prof-specialty,
## relationship=Own-child,
## race=White,
## sex=Male,
## capital-loss=None,
## native-country=United-States} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [3] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## occupation=Prof-specialty,
## relationship=Own-child,
## race=White,
## capital-gain=None,
## capital-loss=None,
## native-country=United-States} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [4] {age=Middle-aged,
## education=Bachelors,
## occupation=Prof-specialty,
## relationship=Own-child,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## native-country=United-States} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [5] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## occupation=Prof-specialty,
## relationship=Own-child,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## native-country=United-States} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1
## [6] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## relationship=Own-child,
## race=White,
## sex=Male,
## capital-gain=None,
## capital-loss=None,
## native-country=United-States} => {hours-per-week=Full-time} 0.1666667 1 0.1666667 1.5 1summary(head(conf.sort.rule))## set of 6 rules
##
## rule length distribution (lhs + rhs):sizes
## 2
## 6
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 2 2 2 2 2
##
## summary of quality measures:
## support confidence coverage lift count
## Min. :0.1667 Min. :1 Min. :0.1667 Min. :1.5 Min. :1
## 1st Qu.:0.1667 1st Qu.:1 1st Qu.:0.1667 1st Qu.:1.5 1st Qu.:1
## Median :0.1667 Median :1 Median :0.1667 Median :1.5 Median :1
## Mean :0.1667 Mean :1 Mean :0.1667 Mean :1.5 Mean :1
## 3rd Qu.:0.1667 3rd Qu.:1 3rd Qu.:0.1667 3rd Qu.:1.5 3rd Qu.:1
## Max. :0.1667 Max. :1 Max. :0.1667 Max. :1.5 Max. :1
##
## mining info:
## data ntransactions support confidence
## df2 6 0.1 0.8
## call
## apriori(data = df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "hours-per-week=Full-time"))summary(tail(conf.sort.rule))## set of 6 rules
##
## rule length distribution (lhs + rhs):sizes
## 10
## 6
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10 10 10 10 10 10
##
## summary of quality measures:
## support confidence coverage lift count
## Min. :0.1667 Min. :1 Min. :0.1667 Min. :1.5 Min. :1
## 1st Qu.:0.1667 1st Qu.:1 1st Qu.:0.1667 1st Qu.:1.5 1st Qu.:1
## Median :0.1667 Median :1 Median :0.1667 Median :1.5 Median :1
## Mean :0.1667 Mean :1 Mean :0.1667 Mean :1.5 Mean :1
## 3rd Qu.:0.1667 3rd Qu.:1 3rd Qu.:0.1667 3rd Qu.:1.5 3rd Qu.:1
## Max. :0.1667 Max. :1 Max. :0.1667 Max. :1.5 Max. :1
##
## mining info:
## data ntransactions support confidence
## df2 6 0.1 0.8
## call
## apriori(data = df2, parameter = list(supp = 0.1, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "hours-per-week=Full-time"))[Q.N.20] Plot the “hours.per.week.ft.rule” with arulesViz package with plot, plot with “two-key plot”, engine=”plotly”, method=graph & engine=htmlwidget and paraller coordinate plot and interpret each graph carefully
library(arulesViz)plot(hours.per.week.ft.rule)## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(hours.per.week.ft.rule, method = "two-key plot")## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(hours.per.week.ft.rule, engine = "plotly")## Warning: Too many rules supplied. Only plotting the best 1000 using
## 'lift' (change control parameter max if needed).## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.subrules <- head(hours.per.week.ft.rule, n = 10, by
= "confidence")
plot(subrules, method = "graph",
engine = "htmlwidget")subrules <- head(hours.per.week.ft.rule, n = 10, by
= "confidence")
plot(subrules, method = "paracoord") 